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Record W2170226164 · doi:10.1676/09-069.1

Paternal Song Complexity Predicts Offspring Sex Ratios Close to Fledging, but not Hatching, in Song Sparrows

2010· article· en· W2170226164 on OpenAlexafffund
Dominique A. Potvin, Elizabeth A. MacDougall‐Shackleton

Bibliographic record

VenueThe Wilson Journal of Ornithology · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOffspringBiologyFledgeSex ratioMatingSex allocationDemographyHatchingPopulationZoologyReproductive successEcologyGeneticsPregnancy

Abstract

fetched live from OpenAlex

Sex allocation theory predicts that population sex ratios should be generally stable and close to unity, but individuals may benefit by adjusting the sex ratio of their offspring. For example, females paired with attractive males may benefit by overproducing sons relative to daughters, as sons inherit their fathers' attractive ornaments (“sexy son” hypothesis). Similarly, if compatible gene effects on fitness are more pronounced in males than females, genetically dissimilar mated pairs may enhance fitness by overproducing sons (“outbred son” hypothesis). We tested these hypotheses in Song Sparrows (Melospiza melodia) by examining offspring sex ratios of 64 complete families shortly after hatching (“early-stage”) and again shortly before fledging (“late-stage”) in relation to paternal song complexity and the genetic similarity of social mates. Neither early nor late-stage offspring sex ratio was related to parental genetic similarity. Nests of males with larger song repertoires contained more male-biased broods by the late-stage nestling period, but not in the early-stage nestling period. These findings suggest that attractive males may be better able to successfully raise male-biased broods, but not that females adaptively adjust primary sex ratios in response to their social mate's attractiveness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.268
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2010
Admission routes2
Has abstractyes

Explore more

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